Summary of Adaptive Global-local Representation Learning and Selection For Cross-domain Facial Expression Recognition, by Yuefang Gao et al.
Adaptive Global-Local Representation Learning and Selection for Cross-Domain Facial Expression Recognition
by Yuefang Gao, Yuhao Xie, Zeke Zexi Hu, Tianshui Chen, Liang Lin
First submitted to arxiv on: 20 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed AGLRLS framework addresses limitations in cross-domain facial expression recognition (CD-FER) by incorporating global-local adversarial adaptation, semantic-aware pseudo label generation, and prediction consistency learning. The framework learns domain-invariant features through global-local adversarial adaptation, while also promoting discriminative feature representation during training. A novel dynamic threshold strategy is employed to generate reliable pseudo labels for model optimization. Inference performance is improved through a global-local prediction consistency module that learns optimal results from multiple predictions. Experimental results demonstrate the effectiveness of the proposed framework, outperforming current competing methods by a significant margin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to recognize facial expressions in pictures taken in different places or situations. This task is hard because pictures can look very different depending on where they were taken. The authors suggest a special kind of learning that helps the computer understand both big and small features in the picture, which makes it better at recognizing emotions. They also use fake labels to help the computer learn what’s important and what’s not. This approach is tested with many pictures and shows great results. |
Keywords
» Artificial intelligence » Inference » Optimization
